Context: Resource movements across ecosystem
boundaries are important determinants of the diversity
and abundance of organisms in the donor and recipient
ecosystem. However the effects of cross-ecosystem
movements of materials at broader spatial extents than
a typical field study are not well understood.
Objectives: We tested the hypotheses that (1) variation
in abundance of 57 forest songbird species within
four foraging guilds is explained by modeled emergent
aquatic insect biomass inputs from adjacent lakes and
streams and (2) the degree of association varies across
foraging guilds and species within guilds. We also
sought to determine the importance of emergent
aquatic insects while accounting for variation in local
forest cover and edge.
Methods: We spatially modeled the degree to which
distribution and abundance of songbirds in different
foraging guilds was explained by modeled emergent
aquatic insect biomass. We used multilevel models to
simultaneously estimate the responses of species in
four different insectivorous guilds. Bird abundance
was summarized from point counts conducted over
24 years at 317 points.
Results: Aerial insectivores were more abundant in
areas with high estimated emergent insect biomass
inputs to land (regression coefficient 0.30, P\0.05)
but the overall abundance of gleaners, bark-probers,
and ground-foragers was not explained by estimated
emergent insect abundance. The coursing aerial
insectivores had the strongest association with emergent
insects followed by willow flycatcher, olive-sided
flycatcher, and alder flycatcher.
Conclusions: Modeling cross-ecosystem movements
of materials at broad spatial extents can effectively
characterize the importance of this ecological process
for aerial insectivorous songbirds.
File: Schilke-et-al_2020_Landscape-Ecology_Modeling-a-cross-system-subsidy.pdf
Climate change is altering patterns of resource availability and this may have negative effects on insectivorous forest birds in the US upper Midwest. As invertebrate life cycle phenology shifts due to earlier spring leaf-out, nesting birds are vulnerable to phenological mismatches between food supply and demand. Areas with complex topography, and thus a variety of thermal and humidity conditions, may support a greater variety of plant and invertebrate phenological rates and stages within close proximity than are found in areas with simple topography. However, the extent and magnitude of this phenomenon is unclear, as is the degree to which topographic position may influence the ability of species to persist during extreme conditions. We examined the effects of topographic position on the
phenology of a tri-trophic forest system over two years from spring through mid-summer. We hypothesized that in cool microsites the likelihood of trophic mismatches and late season food shortages is lower than in warm microsites. At 70 sites in the Baraboo Hills, part of the Driftless Area of the US Midwest, we recorded leaf-out timing of over 700 deciduous trees, measured weekly changes in invertebrate biomass on understory foliage, and conducted bird point counts to assess avian species richness and density. In stream gorges, cooler temperatures were associated with slight but significant delays in leaf-out timing of canopy and understory deciduous trees relative to upland sites. At all sites, invertebrate biomass was distributed relatively evenly across the study period, in contrast to other temperate zone sites where phenological mismatches have been reported between birds and their invertebrate prey. Invertebrate
biomass was similar in stream gorges and uplands in both study years. Insectivorous bird species richness was greater in stream gorges than in the surrounding upland forest during both seasons and was positively related to Lepidoptera larvae biomass in the understory. Among eight abundant insectivorous bird species, density was similar in uplands and stream gorges, among four species density was higher in uplands, and density of two species was higher in stream gorges. These results suggest that insectivorous birds within this study area are unlikely to experience trophic mismatches, and that despite having cooler microclimates and higher avian species richness, stream gorges did not provide more invertebrate food resources than uplands under the climate conditions of the years in which we
sampled this tri-trophic system.
File: Persche-and-Pidgeon-2020.pdf
The positive monotonic relationship between habitat heterogeneity and species richness is a cornerstone of ecology. Recently, it was suggested that this relationship should be unimodal rather than monotonic due to a tradeoff between environmental heterogeneity and population sizes, which increases local species extinctions at high heterogeneity levels. Here, we studied the richness–heterogeneity relationship for an avian community using two different environmental variables, foliage-height diversity and cover type diversity. We analyzed the richness–heterogeneity within different habitat types (grasslands, savannas, or woodlands) and at the landscape scale. We found strong evidence that both positive and unimodal relationships exist at the landscape scale. Within habitats we found positive relationships between richness and heterogeneity in grasslands and woodlands, and unimodal relationships in savannas. We suggest that the length of the environmental heterogeneity gradient (which is affected by both spatial scale and the environmental variable being analyzed) affects the type of the richness–heterogeneity relationship. We conclude that the type of the relationship between species richness and environmental heterogeneity is non-ubiquitous, and varies both within and among habitats and environmental variables.
File: Bar-Massada-Wood-2014.pdf
Biodiversity science and conservation alike require environmental indicators to understand species richness and predict species distribution patterns. The Dynamic Habitat Indices (DHIs) are a set of three indices that summarize annual productivity measures from satellite data for biodiversity applications, and include: a) cumulative annual productivity; b) minimum annual productivity; and c) variation in annual productivity. At global scales and in temperate regions the DHIs predict species diversity patterns well, but the DHIs have not been tested in the tropics, where higher levels of productivity lead to the saturation of many remotely sensed vegetation indices. Our goal was to explain bird species richness patterns based on the DHIs in tropical areas. We related the DHIs to species richness of resident landbirds for five guilds (forest, scrub, grassland, generalist, and all resident birds) based on a) species distribution model (SDM) maps for 217 species, and b) range map for 564 species across Thailand. We also quantified the relative importance of the DHIs in multiple regression models that included two measures of topography, and two climate metrics using multiple regression, best-subsets, and hierarchical partitioning analyses. We found that the three DHIs alone explained forest bird richness best (R2adj 0.61 for both SDM- and rangemap based richness; 0.15–0.54 for the other guilds). When combining the DHIs with topography and climate, the richness of both forest birds and all resident bird species was equally well explained (R2adj 0.85 and 0.67 versus 0.81 and 0.68). Among the three DHIs, cumulative annual productivity had the greatest explanatory power for all guilds based on SDM richness maps (R2adj 0.54–0.61). The strong relationship between the DHIs and bird species richness in Thailand suggests that the DHIs capture energy availability well and are useful in biodiversity assessments and potentially bird conservation in tropical areas.
File: Suttidate_etal_RSE_TropicalBirds_DHI_2019.pdf
Biodiversity science and conservation alike require environmental indicators to understand species richness and predict species distribution patterns. The Dynamic Habitat Indices (DHIs) are a set of three indices that summarize annual productivity measures from satellite data for biodiversity applications, and include: a) cumulative annual productivity; b) minimum annual productivity; and c) variation in annual productivity. At global scales and in temperate regions the DHIs predict species diversity patterns well, but the DHIs have not been tested in the tropics, where higher levels of productivity lead to the saturation of many remotely sensed vegetation indices. Our goal was to explain bird species richness patterns based on the DHIs in tropical areas. We related the DHIs to species richness of resident landbirds for five guilds (forest, scrub, grassland, generalist, and all resident birds) based on a) species distribution model (SDM) maps for 217 species, and b) range map for 564 species across Thailand. We also quantified the relative importance of the DHIs in multiple regression models that included two measures of topography, and two climate metrics using multiple regression, best-subsets, and hierarchical partitioning analyses. We found that the three DHIs alone explained forest bird richness best (R2adj 0.61 for both SDM- and rangemap based richness; 0.15–0.54 for the other guilds). When combining the DHIs with topography and climate, the richness of both forest birds and all resident bird species was equally well explained (R2adj 0.85 and 0.67 versus 0.81 and 0.68). Among the three DHIs, cumulative annual productivity had the greatest explanatory power for all guilds based on SDM richness maps (R2adj 0.54–0.61). The strong relationship between the DHIs and bird species richness in Thailand suggests that the DHIs capture energy availability well and are useful in biodiversity assessments and potentially bird conservation in tropical areas.
Identifying the factors that determine habitat suitability and hence patterns of wildlife abundances
over broad spatial scales is important for conservation. Ecosystem productivity is a key aspect of habitat
suitability, especially for large mammals. Our goals were to a) explain patterns of moose (Alces alces)
abundance across Russia based on remotely sensed measures of vegetation productivity using Dynamic
Habitat Indices (DHIs), and b) examine if patterns of moose abundance and productivity difered before
and after the collapse of the Soviet Union. We evaluated the utility of the DHIs using multiple regression
models predicting moose abundance by administrative regions. Univariate models of the individual
DHIs had lower predictive power than all three combined. The three DHIs together with environmental
variables, explained 79% of variation in moose abundance. Interestingly, the predictive power of the
models was highest for the 1980s, and decreased for the two subsequent decades. We speculate that
the lower predictive power of our environmental variables in the later decades may be due to increasing
human infuence on moose densities. Overall, we were able to explain patterns in moose abundance in
Russia well, which can inform wildlife managers on the long-term patterns of habitat use of the species.
File: Razenkova_etal_SciReports_Moose_2020.pdf
Identifying the factors that determine habitat suitability and hence patterns of wildlife abundances
over broad spatial scales is important for conservation. Ecosystem productivity is a key aspect of habitat
suitability, especially for large mammals. Our goals were to a) explain patterns of moose (Alces alces)
abundance across Russia based on remotely sensed measures of vegetation productivity using Dynamic
Habitat Indices (DHIs), and b) examine if patterns of moose abundance and productivity difered before
and after the collapse of the Soviet Union. We evaluated the utility of the DHIs using multiple regression
models predicting moose abundance by administrative regions. Univariate models of the individual
DHIs had lower predictive power than all three combined. The three DHIs together with environmental
variables, explained 79% of variation in moose abundance. Interestingly, the predictive power of the
models was highest for the 1980s, and decreased for the two subsequent decades. We speculate that
the lower predictive power of our environmental variables in the later decades may be due to increasing
human infuence on moose densities. Overall, we were able to explain patterns in moose abundance in
Russia well, which can inform wildlife managers on the long-term patterns of habitat use of the species.
Ecotourism is developing rapidly in biodiversity hotspots worldwide, but there is limited and mixed
empirical evidence that ecotourism achieves positive biodiversity outcomes. We assessed whether ecotourism
influenced forest loss rates and trajectories from 2000 to 2017 in Himalayan temperate forests. We compared forest
loss in 15 ecotourism hubs with nonecotourism areas in 4 Himalayan countries. We used matching statistics to
control for local-level determinants of forest loss, for example, population density, market access, and topography.
None of the ecotourism hubs was free of forest loss, and we found limited evidence that forest-loss trajectories in
ecotourism hubs were different from those in nonecotourism areas. In Nepal and Bhutan, differences in forest loss
rates between ecotourism hubs and matched nonecotourism areas did not differ significantly, and the magnitude
of the estimated effect was small. In India, where overall forest loss rates were the lowest of any country in
our analysis, forest loss rates were higher in ecotourism hubs than in matched nonecotourism areas. In contrast,
in China, where overall forest loss rates were highest, forest loss rates were lower in ecotourism hubs than
where there was no ecotourism. Our results suggest that the success of ecotourism as a forest conservation
strategy, as it is currently practiced in the Himalaya, is context dependent. In a region with high deforestation
pressures, ecotourism may be a relatively environmentally friendly form of economic development relative to
other development strategies. However, ecotourism may stimulate forest loss in regions where deforestation rates
are low.
File: Brandt_etal_-ConsBio_2019.pdf
Ecotourism is developing rapidly in biodiversity hotspots worldwide, but there is limited and mixed
empirical evidence that ecotourism achieves positive biodiversity outcomes. We assessed whether ecotourism
influenced forest loss rates and trajectories from 2000 to 2017 in Himalayan temperate forests. We compared forest
loss in 15 ecotourism hubs with nonecotourism areas in 4 Himalayan countries. We used matching statistics to
control for local-level determinants of forest loss, for example, population density, market access, and topography.
None of the ecotourism hubs was free of forest loss, and we found limited evidence that forest-loss trajectories in
ecotourism hubs were different from those in nonecotourism areas. In Nepal and Bhutan, differences in forest loss
rates between ecotourism hubs and matched nonecotourism areas did not differ significantly, and the magnitude
of the estimated effect was small. In India, where overall forest loss rates were the lowest of any country in
our analysis, forest loss rates were higher in ecotourism hubs than in matched nonecotourism areas. In contrast,
in China, where overall forest loss rates were highest, forest loss rates were lower in ecotourism hubs than
where there was no ecotourism. Our results suggest that the success of ecotourism as a forest conservation
strategy, as it is currently practiced in the Himalaya, is context dependent. In a region with high deforestation
pressures, ecotourism may be a relatively environmentally friendly form of economic development relative to
other development strategies. However, ecotourism may stimulate forest loss in regions where deforestation rates
are low.
Remotely sensed data can estimate terrestrial productivity more consistently and comprehensively across large
areas than field observations. However, questions remain how species richness and abundances are related to
terrestrial productivity in different biogeographic realms. The Dynamic Habitat Indices (DHIs) are a set of three
remote sensing indices each related to a key biodiversity productivity hypothesis (i.e., available energy proxied by
the annual cumulative productivity, environmental stress proxied by the minimum productivity throughout the
year, and environmental stability proxied by the annual coefficient of variation in productivity). Here, we quantify
the relevance of each hypothesis globally and for different biogeographic realms using models of species richness
for three taxa (amphibians, birds, and mammals) derived from IUCN species range maps. Using parameterized
generalized additive models (GAM’s) we found that the available energy hypothesis was the best individual
index explain 37–43% of the variation in species richness globally with the best models for amphibians and
worst for mammal richness. Examining the residuals of these GAMS indicated that adding the environmental
stress hypothesis explained 0–22% additional variance, especially in the Nearctic where large amounts of snow
and ice are prevalent and environmental conditions deteriorate during winter. The addition of the environmental
stability hypothesis generally explained more variance than the environmental stress hypothesis, especially in
the Neartic and Paleartic and for birds however, in certain cases, the environmental stress hypothesis explains
more variance at the realm scale.
File: Coops_etal_EcoIndicators_2019.pdf
Remotely sensed data can estimate terrestrial productivity more consistently and comprehensively across large
areas than field observations. However, questions remain how species richness and abundances are related to
terrestrial productivity in different biogeographic realms. The Dynamic Habitat Indices (DHIs) are a set of three
remote sensing indices each related to a key biodiversity productivity hypothesis (i.e., available energy proxied by
the annual cumulative productivity, environmental stress proxied by the minimum productivity throughout the
year, and environmental stability proxied by the annual coefficient of variation in productivity). Here, we quantify
the relevance of each hypothesis globally and for different biogeographic realms using models of species richness
for three taxa (amphibians, birds, and mammals) derived from IUCN species range maps. Using parameterized
generalized additive models (GAM’s) we found that the available energy hypothesis was the best individual
index explain 37–43% of the variation in species richness globally with the best models for amphibians and
worst for mammal richness. Examining the residuals of these GAMS indicated that adding the environmental
stress hypothesis explained 0–22% additional variance, especially in the Nearctic where large amounts of snow
and ice are prevalent and environmental conditions deteriorate during winter. The addition of the environmental
stability hypothesis generally explained more variance than the environmental stress hypothesis, especially in
the Neartic and Paleartic and for birds however, in certain cases, the environmental stress hypothesis explains
more variance at the realm scale.
The thermal environment limits species ranges through its influence on physiology and resource distributions
and thus affects species richness patterns over broad spatial scales. Understanding how temperature drives
species richness patterns is particularly important in the context of global change and for effective conservation
planning. Landsat 8's Thermal Infrared Sensor (TIRS) allows direct mapping of temperature at moderate spatial
resolutions (100 m, downscaled by the USGS to 30 m), overcoming limitations inherent in coarse interpolated
weather station data that poorly capture fine-scale temperature patterns over broad areas. TIRS data thus offer
the unique opportunity to understand how the thermal environment influences species richness patterns. Our
aim was to develop and assess the ability of TIRS-based temperature metrics to predict patterns of winter bird
richness across the conterminous United States during winter, a period of marked temperature stress for birds.
We used TIRS data from 2013-2018 to derive metrics of relative temperature and intra-seasonal thermal heterogeneity.
To quantify winter bird richness across the conterminous US, we tabulated the richness only for
resident bird species, i.e., those species that do not move between the winter and breeding seasons, from the
North American Breeding Bird Survey, the most extensive survey of birds in the US. We expected that relative
temperature and thermal heterogeneity would have strong positive associations with winter bird richness because
colder temperatures heighten temperature stress for birds, and thermal heterogeneity is a proxy for
thermal niches and potential thermal refugia that can support more species. We further expected that both the
strength of the effects and the relative importance of these variables would be greater for species with greater
climate sensitivity, such as small-bodied species and climate-threatened species (i.e., those with large discrepancies
between their current and future distributions following projected climate change). Consistent with
our predictions, relative temperature and thermal heterogeneity strongly positively influenced winter bird
richness patterns, with statistical models explaining 37.3% of the variance in resident bird richness. Thermal
heterogeneity was the strongest predictor of small-bodied and climate-threatened species in our models, whereas
relative temperature was the strongest predictor of large-bodied and climate-stable species. Our results demonstrate
the important role that the thermal environment plays in governing winter bird richness patterns and
highlight the previously underappreciated role that intra-seasonal thermal heterogeneity may have in supporting
high winter bird species richness. Our findings thus illustrate the exciting potential for TIRS data to guide
conservation planning in an era of global change.
File: Elsen_et-al_2020_Landsat-8_winterbirdrichness_US.pdf
